Abstract
Algal demographic models have been developed mainly to study their life cycle evolution or optimize their commercial exploitation. Most commonly, structured-aggregated population models simulate the main life cycle stages considering their fertility, growth and survival. Their coarse resolution results in weak predictive abilities since neglected details may still impact the whole. In our case, we need a model of Agarophyton chilense natural intertidal populations that unravels the complex demography of isomorphic biphasic life cycles and be further used for: (i) introduction of genetics, aimed at studying the evolutionary stability of life cycles, (ii) optimizing commercial exploitation, and (iii) adaptation for other species. Long-term monitoring yield 6,066 individual observations and 40 population observations. For a holistic perspective, we developed an Individual-Based Model (IBM) considering ploidy stage, sex stage, holdfast age and survival, frond size, growth and breakage, fecundity, spore survival, stand biomass, location and season. The IBM was calibrated and validated comparing observed and estimated sizes and abundances of gametophyte males, gametophyte females and tetrasporophytes, stand biomass, haploid:dipoid ratio (known as H:D or G:T), fecundity and recruitment. The IBM replicated well the respective individual and population properties, and processes such as winter competition for light, self-thinning, summer stress from desiccation, frond breakage and re-growth, and different niche occupation by haploids and diploids. Its success depended on simulating with precision details such as the holdfasts’ dynamics. Because “details” often occur for a reduced number of individuals, inferring about them required going beyond statistically significant evidences and integrating these with parameter calibration aimed at maximized model fit. On average, the population was haploid-dominated (H:D > 1). In locations stressed by desiccation, the population was slightly biased toward the diploids and younger individuals due to the superior germination and survival of the diploid sporelings. In permanently submerged rock pools the population was biased toward the haploids and older individuals due to the superior growth and survival of the haploid adults. The IBM application demonstrated that conditional differentiation among ploidy stages was responsible for their differential niche occupation, which, in its turn, has been argued as the driver of the evolutionary stability of isomorphic biphasic life cycles.
Highlights
Seaweeds are the most efficient primary producers on the planet (Creed et al, 2019)
Modeling the demography of algal populations has basically been done by structured-aggregated population models, which can be sub-divided into two classes: matrix models (Aberg, 1992; Scrosati and DeWreede, 1999; Engel et al, 2001; Engelen and Santos, 2009; Vieira and Santos, 2010, 2012a,b; Vieira and Mateus, 2014) and ordinary difference equation models (Richerd et al, 1993; Hughes and Otto, 1999; Thornber and Gaines, 2004; Fierst et al, 2005)
The closer that we found to our study was the Individual-Based Models (IBM) run by Chave et al (2002) demonstrating that the biodiversity of sessile organisms was maintained by processes such as conditional differentiation and recruitment limitation
Summary
Seaweeds are the most efficient primary producers on the planet (Creed et al, 2019). In coastal waters, they often sustain the food web (Kang et al, 2008; Cordone et al, 2018; Momo et al, 2020), structure the community (Arkema et al, 2009; Momo et al, 2020) and are of commercial interest for mankind (Bustamante and Castilla, 1990; Guillemin et al, 2008; Lawton et al, 2013; Veeragurunathan et al, 2015; Mantri et al, 2020). Modeling the demography of algal populations has basically been done by structured-aggregated population models, which can be sub-divided into two classes: matrix models (Aberg, 1992; Scrosati and DeWreede, 1999; Engel et al, 2001; Engelen and Santos, 2009; Vieira and Santos, 2010, 2012a,b; Vieira and Mateus, 2014) and ordinary difference equation models (Richerd et al, 1993; Hughes and Otto, 1999; Thornber and Gaines, 2004; Fierst et al, 2005). Writing the model in ordinary difference equation notation facilitates the deduction of Jacobian matrices, equilibrium points and other useful tools (see Hughes and Otto, 1999; Thornber and Gaines, 2004; Fierst et al, 2005; Bessho and Otto, 2020) These models are structured by state variables. Generalizing while disregarding details may undermine the homogeneity within state variables and lead to biased forecasts
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